Method and system for acoustic data selection for training the parameters of an acoustic model

ABSTRACT

A system and method are presented for acoustic data selection of a particular quality for training the parameters of an acoustic model, such as a Hidden Markov Model and Gaussian Mixture Model, for example, in automatic speech recognition systems in the speech analytics field. A raw acoustic model may be trained using a given speech corpus and maximum likelihood criteria. A series of operations are performed, such as a forced Viterbi-alignment, calculations of likelihood scores, and phoneme recognition, for example, to form a subset corpus of training data. During the process, audio files of a quality that does not meet a criterion, such as poor quality audio files, may be automatically rejected from the corpus. The subset may then be used to train a new acoustic model.

BACKGROUND

The present invention generally relates to telecommunication systems andmethods, as well as automatic speech recognition systems. Moreparticularly, the present invention relates to selecting acoustic dataof a particular quality for training the parameters of an acousticmodel, such as a Hidden Markov Model and Gaussian Mixture Model(HMM-GMM), for example, within automatic speech recognition systems.

SUMMARY

A system and method are presented for acoustic data selection of aparticular quality for training the parameters of an acoustic model,such as a Hidden Markov Model and Gaussian Mixture Model, for example,in automatic speech recognition systems in the speech analytics field. Araw acoustic model may be trained using a given speech corpus andmaximum likelihood criteria. A series of operations are performed, suchas a forced Viterbi-alignment, calculations of likelihood scores, andphoneme recognition, for example, to form a subset corpus of trainingdata. During the process, audio files of a quality that does not meet acriterion, such as poor quality audio files, may be automaticallyrejected from the corpus. The subset may then be used to train a newacoustic model.

In one embodiment, a method is presented for training models in speechrecognition systems through the selection of acoustic data comprisingthe steps of: training an acoustic model; performing a forced Viterbialignment; calculating a total likelihood score; performing a phonemerecognition; retaining selected audio files; and training a new acousticmodel.

In one embodiment, a system for training models in speech recognitionsystems through the selection of acoustic data comprising: means fortraining an acoustic model; means for performing a forced Viterbialignment; means for calculating a total likelihood score; means forperforming a phoneme recognition; means for retaining selected audiofiles; and means for training a new acoustic model.

In one embodiment, a method is provided for training an acoustic modelin an automatic speech recognition system comprising the steps of:training a set of raw data using a given speech corpus and the maximumlikelihood criteria; performing a forced Viterbi-alignment; calculatinga total likelihood score; performing phoneme recognition on audio filesin said corpus; retaining selected audio files; forming a subset corpusof training data; and training a new acoustic model with said subset.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating the basic components of an embodimentof the system.

FIG. 2 is a flowchart illustrating an embodiment of the selectionprocess.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of theinvention, reference will now be made to the embodiment illustrated inthe drawings and specific language will be used to describe the same. Itwill nevertheless be understood that no limitation of the scope of theinvention is thereby intended. Any alterations and further modificationsin the described embodiments, and any further applications of theprinciples of the invention as described herein are contemplated aswould normally occur to one skilled in the art to which the inventionrelates.

Automatic speech recognition (ASR) systems analyze human speech andtranslate it into text or words. There are many considerations regardinghow well an ASR system may perform. Performance of these systems iscommonly evaluated based on factors such as accuracy, reliability,language support, and the speed with which speech can be recognized. Ingeneral, it is expected that the performance of the system be very high.Additional factors such as accent, articulation, speech rate,pronunciation, background noise, etc., may have a negative effect on theaccuracy of the system. In situations involving a large corpus ofconversations, processing speed is necessary to analyze large numbers oftelephone conversations at once and in real-time. The system is expectedto perform consistently and reliably irrespective of channel conditionsand various artifacts introduced by modern telephony channels,especially voice over IP.

To train a HMM-GMM, the process may begin, in one embodiment, with ahuman-transcribed speech training-data corpus. This corpus may consistof several speech audio files along with the transcription of thesentence(s) spoken in a particular audio file. The HMM-GMM trainingalgorithm may convert the sentence to a sequence of phonemes thatcorresponds to the words in the sentence using a pronunciationdictionary. Concurrently, a sequence of feature vectors may be extractedfrom a signal of some length from the corresponding audio files. Thewindowing operation is advanced by some time interval to obtain the nextfeature vector until the end of the audio file is reached. A sequence ofphonemes and a sequence of feature vectors are obtained for each audiofile, which are in turn used to train the HMM-GMM.

Those skilled in the art will recognize from the present disclosure thatthe various methodologies disclosed herein may be computer implementedusing many different forms of data processing equipment, for example,digital microprocessors and associated memory executing appropriatesoftware program(s).

FIG. 1 is a diagram illustrating an embodiment of the basic componentsof a system, 100. The basic components of a system 100 may include:Knowledge Sources 105, which may include an Acoustic Model 110 and aPronunciation Dictionary/Predictor 115; an Audio Stream 120; a Front EndFeature Calculator 125; a Speech Recognition Engine 130; and Results135.

A phoneme is assumed to be the basic unit of sound. A predefined set ofsuch phonemes is assumed to completely describe all sounds of aparticular language. The Knowledge Sources 105 may store probabilisticmodels, for example, HMM-GMM, of relations between pronunciations(phonemes) and acoustic events, such as a sequence of feature vectorsextracted from the speech signal. An HMM encodes the relationship of theobserved audio signal and the unobserved phonemes. A training processmay then study the statistical properties of the feature vectors emittedby a Hidden Markov Model (HMM) state corresponding to a given phonemeover a large collection of transcribed training-data. An emissionprobability density for the feature vector in a given HMM state of aphoneme is learned (also called acoustic model training) through thetraining process. More specifically, training is performed for atriphone. An example of a triphone may be a tuple of three phonemes inthe phonetic transcription sequence corresponding to a center phone.Several HMM states of triphones are tied together to share a commonemission probability density function. Typically, the emissionprobability density function is modeled using a Gaussian mixture model(GMM). A set of these GMMs and HMMs is termed as an acoustic model.

The Knowledge Sources 105 may be developed by analyzing large quantitiesof audio data. The acoustic model and the pronunciationdictionary/predictor are made, for example, by looking at a word like“hello” and examining the phonemes that comprise the word. Each word inthe speech recognition system is represented by a statistical model ofits constituent sub-word units called the phonemes. The phonemes for“hello”, as defined in a standard phoneme dictionary, are: “hh”, “eh”,“l”, and “ow”. These are then converted to a sequence of triphones, forexample, “sil−hh+eh”, “hh−eh+l”, “eh−l+ow”, and “l−ow+sil”, where “sil”is the silence phone. Finally, as previously described, the HMM statesof all possible triphones are mapped to the tied-states. Tied-states arethe unique states for which acoustic model training is performed. Thesemodels are language dependent. In order to also provide multi-lingualsupport, multiple knowledge sources may be provided.

The acoustic model 110 may be formed by statistically modeling thevarious sounds that occur in a particular language.

The pronunciation dictionary, 115, may be responsible for decomposing aword into a sequence of phonemes. Words presented from the user may bein human readable form, such as grapheme/alphabets of a particularlanguage. However, the pattern matching algorithm may rely on a sequenceof phonemes which represent the pronunciation of the keyword. Once thesequence of phonemes is obtained, the corresponding statistical modelfor each of the phonemes (or the corresponding triphones) in theacoustic model may be examined. A concatenation of these statisticalmodels may be used to perform speech recognition. For words that are notpresent in the dictionary, a predictor, which is based on linguisticrules, may be used to resolve the pronunciations.

The audio stream (i.e., what is spoken into the system by the user),120, may be fed into the front end feature calculator, 125, which mayconvert the audio stream into a representation of the audio stream, or asequence of spectral features. Audio analysis may be performed bycomputation of spectral features, for example, Mel Frequency CepstralCoefficients (MFCC) and/or its transforms.

The signal from the front end feature calculator, 125, may then be fedinto a speech recognition engine, 130. The task of the recognitionengine may be to take a set of words (called lexicon) and search throughpresented audio stream , using the probabilities from the acousticmodel, to determine the most likely sentence spoken in that audiosignal. One example of a speech recognition engine may include but notbe limited to a Keyword Spotting System. For example, in themulti-dimensional space constructed by the feature calculator, a spokenword may become a sequence of MFCC vectors forming a trajectory in theacoustic space. Keyword spotting may now simply become a problem ofcomputing probability of generating the trajectory given the keywordmodel. This operation may be achieved by using the well-known principleof dynamic programming, specifically the Viterbi algorithm, which alignsthe keyword model to the best segment of the audio signal, and resultsin a match score. If the match score is significant, the keywordspotting algorithm infers that the keyword was spoken and reports akeyword spotted event.

The resulting sequence of words 135 may then be reported in real-time.The report may be presented as a start and end time of the keyword inthe audio stream with a confidence value that the keyword was found. Theprimary confidence value may be a function of how the keyword is spoken.For example, in the case of multiple pronunciations of a single word,the keyword “tomato” may be spoken as “te-mah-toh” and “te-may-toh”. Theprimary confidence value may be lower when the word is spoken in a lesscommon pronunciation or when the word is not well enunciated. Thespecific variant of the pronunciation that is part of a particularrecognition is also displayed in the report.

As illustrated in FIG. 2, a process 200 for illustrating an embodimentof a selection process is provided. The process 200 may be operative inthe Acoustic Model 110 of the Knowledge Sources 105 component of thesystem 100 (FIG. 1). An acoustic model may be trained on atrain-data-set of the language using the well-known maximum likelihoodcriterion in the following process.

In step 205, the acoustic model is trained. For example, an HMM-GMMacoustic model, which may be represented as

_(raw), may be trained using the given speech corpus

_(raw) and the maximum likelihood criterion. A sequence of featurevectors X={x₁, x₂, . . . , x_(N)} and the corresponding sequence of HMMstates Q={q₁, q₂, . . . , q_(N)} may be obtained by the availablephonetic transcription of the sentence in the audio signal. The maximumlikelihood acoustic model training may comprise estimating the

_(raw) parameters of the probability distribution that maximizes thelikelihood of the training data given the phonetic transcription.Control is passed to operation 210 and the process 200 continues.

In operation 210, a forced Viterbi-alignment is performed and theaverage frame likelihood score calculated. For example, the forcedViterbi-alignment of the corpus

_(raw) may be performed using the acoustic model

_(raw). As a by-product of the forced alignment, the total likelihoodscore α_(r) for each audio file α_(r) is obtained, where r ∈1, 2, . . ., R. Assuming that the audio file α_(r) consist of f_(r)=N featureframes, i.e., X={x₁, x₂, . . . , x_(N)}, with the underlying forcedaligned HMM states being Q={q₁, q₂, . . . , q_(N)}, the total likelihoodof the audio file α_(r) is given as:

α_(r) =p(x ₁ |q ₁)Π_(i=2) ^(N) P(q _(i) |q _(i−1))p(x _(i) |q _(i))

where P(q_(i)|q_(i−1)) represents the HMM state transition probabilitybetween states ‘i−1’ and ‘i’ and p(x_(i)|q_(i)) represents the stateemission likelihood of the feature vector x_(i) being present in thestate q_(i). All of the audio files α_(r) together form the corpus

_(raw). Assuming ‘f’_(r) frames in the audio file α_(r), an averageframe likelihood score may be obtained using the equation:

$\beta_{r} = \frac{\alpha_{r}}{f_{r}}$

where β_(r) represents the average frame likelihood score. Control ispassed to operation 215 and the process 200 continues.

In operation 215, the total likelihood of the score is calculated andaudio files are rejected. For example, β_(r) may be averaged where r ∈1,2, . . . , R to obtain an average frame likelihood score δ over theentire corpus

_(raw). The value of δ may be indicative of the average framelikelihoods over the entire corpus, which may consist of varying qualityof audio files. This quality may range from very bad, to mediocre, tovery good. The poor quality audio file may either have poor audiocondition, and/or poor articulation by the speaker, and/or poor sentencetranscription by the human transcriber. In one embodiment, the goal isto automatically reject such audio files and their transcription fromthe training-data corpus. This is illustrated in the following equation:

$\delta = {\sum\limits_{r = 1}^{R}\; \frac{\beta_{r}}{R}}$

Control is passed to step 220 and process 200 continues.

In operation 220, phoneme recognition is performed and the averagephoneme recognition accuracy is obtained. In the following example, aphoneme recognition of the audio file α_(r) using the Viterbi search andthe acoustic model

_(raw). The correct phoneme recognition accuracy of each audio file mayalso be estimated with the available manual transcription (ground-truth)of each of the files.

The following equation is used to obtain the average phoneme recognitionaccuracy, denoted by v over the corpus

_(raw):

$v = {\sum\limits_{r = 1}^{R}\; \frac{\gamma_{r}}{R}}$

where γ_(r) represents the accuracy of α_(r).

Control is passed operation 225 and the process 200 continues.

In operation 225, the selected audio files are retained. For example,using the global frame likelihood score δ and the global phonemerecognition accuracy v as reference values, only the audio files α_(g)are retained such that the average frame likelihood β_(g) is above acertain threshold value Δ of the global average δ or its phonemerecognition accuracy γ_(g) is above a certain threshold μ of the globalphoneme recognition accuracy v. Thus, α_(g) is retained if,

β_(g)≥δ+Δ

or,

γ_(g) ≥v+μ

where Δ and μ are user specified thresholds. A user may typicallyspecify that Δ=−0.1×δ and μ=−0.2×v.

A subset of training-data corpus is formed,

_(good), which contains data of the desired quality.

Control is passed to step 230 and process 200 continues.

In operation 230, a new HMM-GMM acoustic model

_(good) is trained using only the data in the corpus

_(good) and the process ends. The new acoustic-model

_(good) may be used in subsequent speech recognition systems.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, the same is to be considered asillustrative and not restrictive in character, it being understood thatonly the preferred embodiment has been shown and described and that allequivalents, changes, and modifications that come within the spirit ofthe inventions as described herein and/or by the following claims aredesired to be protected.

Hence, the proper scope of the present invention should be determinedonly by the broadest interpretation of the appended claims so as toencompass all such modifications as well as all relationships equivalentto those illustrated in the drawings and described in the specification.

Although a very narrow claim is presented herein, it should berecognized the scope of this invention is much broader than presented bythe claim. It is intended that broader claims will be submitted in anapplication that claims the benefit of priority from this application.

1. A method for training models in speech recognition systems throughthe selection of acoustic data comprising the steps of: a. training anacoustic model; b. performing a forced Viterbi alignment; c. calculatinga total likelihood score; d. performing a phoneme recognition; e.retaining selected audio files; and f. training a new acoustic model.2.-32. (canceled)